add model
Browse files- modeling_lddbert.py +16 -5
- pytorch_model.bin +2 -2
modeling_lddbert.py
CHANGED
@@ -383,10 +383,18 @@ class LddBertModel(LddBertPreTrainedModel):
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self.embeddings = Embeddings(config) # Embeddings
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self.transformer = Transformer(config) # Encoder
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self.gru = nn.GRU(config.dim , config.dim//2, config.n_gru_layers, batch_first=True, bidirectional=True)
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for _ in range(config.n_cnn_layers)
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)
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# Initialize weights and apply final processing
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self.post_init()
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@@ -511,9 +519,12 @@ class LddBertModel(LddBertPreTrainedModel):
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gru_output, _ = self.gru(bert_output[0])
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cnn_output =
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output = gru_output + cnn_output
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if not return_dict:
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return (output, ) + bert_output[1:]
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self.embeddings = Embeddings(config) # Embeddings
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self.transformer = Transformer(config) # Encoder
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self.gru = nn.GRU(config.dim , config.dim//2, config.n_gru_layers, batch_first=True, bidirectional=True)
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self.activation_cnn = get_activation('relu')
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self.cnn = nn.ModuleList([
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nn.Sequential(
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nn.Conv2d(in_channels=1,
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out_channels=1,
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kernel_size=config.cnn_kernel_size,
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padding=(config.cnn_kernel_size-1)//2),
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self.activation_cnn
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)
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for _ in range(config.n_cnn_layers)
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])
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# Initialize weights and apply final processing
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self.post_init()
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gru_output, _ = self.gru(bert_output[0])
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cnn_output = bert_output[0].view(input_shape[0], 1, input_shape[1], -1)
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for i, layer_module in enumerate(self.cnn):
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cnn_output = layer_module(cnn_output)
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cnn_output = cnn_output.view(input_shape[0], input_shape[1], -1)
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output = gru_output + cnn_output
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if not return_dict:
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return (output, ) + bert_output[1:]
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pytorch_model.bin
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:189cb4b46e7ca027e3dd89c6f57b1c15e77bc2a58dd1620dcd7dde62d8f42816
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size 331811701
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